A classification device is conventionally known in the art that classifies a content (e.g., an image, a sound, a text, or the like) into a category to which the content belongs. The classification device classifies a content in accordance with a classification condition for classifying the content. The classification condition is generally one that is machine-learned by a machine learning device.
Support Vector Machine (SVM) is known in the art as a machine learning device that learns a classification condition for classifying a content into two categories.
The SVM (machine learning device) uses a set of learning contents that belong to one of two categories and a set of learning contents that belong to the other of the two categories in order to learn beforehand a classification condition (e.g., a function for the classification) for classifying these sets into the two categories. Meanwhile, the classification device classifies an unknown content into either one of the two categories in accordance with the learned classification condition. An SVM technique to separate non-linearly two sets that cannot be separated linearly is disclosed in “GENERALIZED HISTOGRAM INTERSECTION KERNEL FOR IMAGE RECOGNITION”, International Conference on Image Processing (ICIP), September 2005.